The SHAP method is derived from Shapley values in game theory, where
they describe the contribution of each team player in a collaborative environment. In machine learning, it works by assigning each feature an importance value for a particular prediction [1]}. One of the distinguishing factors of SHAP is that the prediction is fairly distributed among features. However some disadvantages according to Molnar  [2]} are that users prefer selective explanations since an overwhelming number of features could create confusion among users. Since Shapley values return a value per feature, no conclusion can be made regarding a change in prediction for changes in the input. Similarly to many permutation-based interpretation methods, the Shapley value method can't classify unrealistic data instances when features are correlated. This works however if features are independent since when a feature is missing it is then marginalized. This is achieved by sampling values from the feature's marginal distribution.
